Open Systems Interconnection (OSI) layers are known to those of skill in the art as a series of protocol layers to define communications in data networks. The first layer relates to the physical aspects of communication. Examples are T-1 and 100-base T. The second layer is called the data link layer. This layer is used to format data passing over a given link. Examples include Ethernet and HDLC. Layer 3 is called the network layer. This layer supports end-to-end packet delivery and the most common example is the IP routing in the Internet. Layer 4, the transport layer, provides end-to-end management of communications.
Networks that use a connection as the primary method of transporting information between two points are considered Layer 4 networks as Layer 4 protocols such as TCP can manage connections directly.
To improve the efficiency of packet networks, label-switching technologies such as frame relay, ATM, and MPLS have become popular for OSI layer 2 wide area networking. The short labels are popular with telecommunication carriers as a more efficient alternative to traditional IP routing. The most popular of these technologies, multi-protocol label switching (MPLS), uses label switched paths “LSPs” to carry packet flows between edge nodes. Packets in these flows are transported in a deterministic, orderly manner. In fact, transport schemes of this nature are so reliable that the term “pseudo wire” has been used to describe this system. Through the use of MPLS, packet flows through LSPs have been used to interconnect LANS (VLANS), support QOS and policy routing, and even switch synchronous services such as DS1s or DS3s.
Neural networks occur naturally and provide the intelligence of the human brain. Artificial Neural Networks (ANNs) are man-made networks used to solve complex problems. Details of these networks are described in the following documents which are incorporated herein by reference:    Reference 1:    Anil K. Jain and Jianchang Mao and K. M. Mohiuddin “Artificial Neural Networks: A Tutorial,” Computer, March, 1996, pp. 31-44. (available online)    Reference 2:    Vipan Kakkar “Comparative Study on Analog and Digital Neural Networks” International Journal of Science and Network Security, VOL. 9 No. 7, July 2009, pp. 14-21. (available online)These two references will aid in providing the reader with the background necessary to understand the neural network aspects of this invention.
Many applications for ANNs exist. An important application lies in the control of the power grid. As this area is complex, a third reference is added.    Reference 3:    U.S. Pat. No. 9,465,397 B2, Forbes
This reference will aid the reader in understanding the complex nature of the power grid, an excellent application for an ANN, and therefore is also incorporated herein by reference.
ANNs and data communication networks have always been treated separately as the requirements and resultant functionality has always been different. ANNs have traditionally been analog networks carrying voltages that are multiplied using analog multipliers to achieve the neural network weighting functions. As these neurons contain no addressing capability, they require a physical connection for communication, and can only communicate with their immediate neighbors. Lately, developers have been using software simulation to build neural networks. These neural networks rely on their host computers which are serial devices and therefore slow compared with true parallel neural networks. What is needed is a neural network technology that can share the connectivity benefits of today's packet networks.